自1950年代DNA结构被破译以来，生物学家长期将人类基因组视为生命的蓝图、代码或电脑程式，然而人类基因组计划揭示仅有不到2%的DNA序列属于真正编码蛋白质的基因。现今科学家认识到，理解基因组的关键不在于单一基因的功能，而在于这些基因如何受到其余非编码DNA的复杂调控，借此让不同细胞类型根据环境讯号随时开启或关闭特定基因组。

在人类等复杂真核生物中，基因调控展现出类似「AND」逻辑的多重组合特性，远比细菌的简单开关复杂。转录因子需成群结队与散布在DNA各处（甚至距离目标基因数百万个核苷酸远）的增强子结合，并透过黏连蛋白拉出染色质环、形成拓扑关联结构域（TADs）与高度动态的转录凝聚体；此外，表观遗传标记、异染色质与常染色质的立体折叠，以及微RNA（microRNA）和剪接体对信使RNA的转录后修饰与替代剪接，共同构成了多层次的动态调控网络。（关键数字：3）

这种高度依赖语境、自我指涉的「奇异环」特性，源于演化过程中对复杂组织控制及抵御转座子等寄生病毒DNA的需求。尽管如AlphaGenome、Evo 2等基因组AI基础模型试图透过序列数据预测生物特征，但因基因组并非静态线性指令，且受饮食、环境与微生物组等「资讯组（informiome）」影响，单靠AI黑盒子难以完全捕捉其全貌；诚如诺贝尔奖得主麦克林托克所言，基因组不是死板的蓝图，而是一个能感知并回应内外环境的「活生生、高度敏感的细胞器官」。

Since the deduction of DNA's structure in the 1950s, biologists have long hailed the human genome as a blueprint, code script, or computer program of life, yet the Human Genome Project revealed that barely 2 percent of our DNA consists of actual protein-coding genes. Today, scientists recognize that the deeper challenge lies not in figuring out what each individual gene does, but in how those genes are regulated by the vast noncoding genome to allow diverse cell types to dynamically switch suites of genes on and off in response to environmental signals.

In complex eukaryotic organisms like humans, gene regulation operates on a combinatorial 'AND' logic that is far more intricate than the simple switches found in bacteria. Transcription factors work in groups to bind with enhancers—some located millions of nucleotides away—brought together via chromatin loops extruded by cohesin, forming topologically associating domains (TADs) and fluid transcription condensates; furthermore, 3D chromatin folding into heterochromatin and euchromatin, epigenetic marks, and post-transcriptional mechanisms like microRNA silencing and alternative splicing by the spliceosome create multiple overlapping layers of control.

This context-dependent, self-referential 'strange loop' evolved both to coordinate diverse tissue types and to defend against parasitic viral sequences known as transposable elements. Although genomic AI foundation models like AlphaGenome and Evo 2 attempt to predict organismal traits from sequence correlations alone, they face inherent limitations because the genome is not a static linear script and interacts heavily with extra-genetic factors like diet, environment, and the microbiome—the 'informiome'; ultimately, as Nobel laureate Barbara McClintock envisioned, the genome is not a rigid blueprint, but a living, exquisitely sensitive organ of the cell.